In this paper, we intend to offer a new method based on fuzzy neural networks for finding a real solution of fuzzy equations system. Our proposed fuzzified neural network is a fivelayer feed-back neural network that corresponding connection weights to output layer are fuzzy numbers. The proposed architecture of artificial neural network, can get a real input vector and calculates it’s correspon...

This paper proposes the neural network solution to the indirect vector control of three phase induction motor including an adaptive neuro fuzzy controller. The basic equations and elements of the indirect vector control scheme are given. The proposed control scheme is realized by an adaptive neuro-fuzzy controller and two feed forward neural network. The neuro-fuzzy controller incorporates fuzz...

a reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. in this study, a new approach based on the adaptive neural fuzzy inference system (anfis) is used to predict the shear strength of reinforced concrete (rc) deep beams. a constitutive relationship was obtained correlating the ultimate load with seven mechanical a...

gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. the main purpose of this study is investigating phenomenon of gas hyd...

This paper reviews neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper deenition, and still has the avour of a buzzword to it. Surprisingly few neuro-fuzzy approaches do actually employ neural networks, even though they are very often depicted ...

Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consi...

In this paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to ...

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...

Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical traini...